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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Alshehri, Yasser West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350351507

Cybersecurity is the top priority for most businesses and government agencies. Teaching students practical cybersecurity is one of the challenges for most academic institutions. Some institutions focus on theory and neglect the practical, which is more appealing to students. Besides, it equips students with the skills the industry needs and reduces the gap between academic institutions and the industry. National Cyber League NCL provides a platform where all students nationwide can join and practice. In the fall of 2023, West Virginia University cybersecurity students joined the NCL competition as part of the course requirements. Participating in team and individual competitions was considered a substitute for the project for the course, which consists of 30% of the total grade. In this paper, we evaluated their background and skills before entering the competition (Who they are?), their experience in the competition, their achievements and skills after completing the competition (what they learned?), and how they evaluate their experience (what they think?). In this paper, we measure their involvement with the competition and how the competition assists them in developing their skills. We rely on their opinion and compare that with their performance according to the report card produced by NCL for every team and each individual. We also found that most students were involved between 10 to 30 hours during the semester with the games. Few students were more than 30 hours during the semester involved with NCL. We found that students were reasonably engaged in the competitions in different areas. Students spent more time in the gymnasium and practice games than in actual competitions. Students achieved an average completion of 35% with 73% average accuracy in the gymnasium, average completion was 19%, and the average accuracy was 76% in the practice game, average completion was 29%, and the average accuracy was 60% in the individual games, and 38% average completion an

关键词： Students

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FLIGHT:Federated Learning with IRS for Grouped Heterogeneous Training

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Journal of Communications and information Networks 2022年第2期7卷 135-144页

作者： Tong Yin Lixin Li Donghui Ma Wensheng Lin Junli Liang Zhu Han School of Electronics and Information Northwestern Poly technical UniversityXi'an 710129China Department of Electrical and Computer Engineering at the University of Houston HoustonTX 77004USAand also with the Department of Computer Science and EngineeringKyung Hee UniversitySeoul 446-701South Korea

In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data ***,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in *** addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication *** this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the ***,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL *** simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous *** the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.

关键词： federated learning decentralized aggrega-tion intelligent reflecting surfaces grouped learning

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Validation of High Angular Resolution Diffusion-Weighted Imaging Protocols 15

Validation of High Angular Resolution Diffusion-Weighted Ima...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Seth, Jyoti Savitha, R. Kumar, Ashwini Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Arka Jain University Department of Mechanical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

High angular resolution diffusion-weighted imaging (HARDI) protocols are strategies used to growth the number of facts amassed about tissue microstructure in diffusion imaging. Current studies have sought to apprehend the validity of HARDI methods and their ability to reconstruct the underlying tissue systems accurately. The evaluation of HARDI reliability is accomplished thru 3 major classes: voxel-wise checks of methods, simulations to evaluate protocol efficiency, and human brain tract reconstruction. outcomes from these studies have tested that HARDI may additionally offer progressed visualization of mind microstructure and might higher differentiate between individual white count tracts and distinctive tissue kinds than conventional diffusion imaging. However, due to the complexity of HARDI protocols, further validation is essential to ensure accuracy and discover the strengths and weaknesses of the strategies. © 2024 IEEE.

关键词： Accomplished Accuracy Distinctive Essential Microstructure Protocols Strategies Validation

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

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computer Systems science & engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn *** use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

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Forecasting the Impact of Deep Learning on Data science Practices: A Comprehensive Analysis 15

Forecasting the Impact of Deep Learning on Data Science Prac...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Acharjya, Kalyan Devaraj Verma, C. Umamaheswari, B. Shelke, Priya M. Kumar, Mukesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is miles information scientists are increasingly using the accuracy and efficiency of information science initiatives. Furthermore, the era has stepped forward hastily to one of these excessive levels. Many leading facts scientists throughout numerous domains have begun to contain deep mastering into their facts technological know-how workflows. In this paper, we examine and forecast the modern-day and destiny impact of deep gaining knowledge of data technology practices. We start by analyzing the distinct advantages of deep learning, including progressed accuracy, reduced guide attempt, more suitable automation, and advanced scalability. We then become aware of extraordinary applications in which deep learning is being applied and evaluate its impact on the respective information science pipelines. Sooner or later, we provide a comprehensive forecast of the capability applications of deep getting to know within the records technology subject inside the near and far destiny. Notably, we recall the capacity of deep studying to force essential modifications in how information mining, analytical choice-making, and large records packages. Through our analysis, we will forecast the ability of deep studying to revolutionize statistics technological know-how practices in extraordinary ways. © 2024 IEEE.

关键词： Automation Practices Processing Technology Unprecedented

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Emerging Wireless Technologies for Efficient Routing for Internet of Things 11th

Emerging Wireless Technologies for Efficient Routing for Int...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： John, Jomy Dhamodaran, S. Ramesh, J.V.N. Anand, Rohit Namdev, Arpit Gupta, Ankur Department of Computer Science P M Government College Kerala Chalakudy India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India New Delhi110020 India Department of Information Technology University Institute of Technology RGPV Madhya Pradesh Bhopal India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819717231

Many types of wireless networks and technologies, including those used for longer distances, shorter distances, and in the personal area are all included into the designs of IoT applications. This opens up a wide range of commercial possibilities in sectors as varied as e-health, smart cities, and smart homes. This article takes a close look at some key wireless technologies for the IoT, as well as several others that are currently undergoing rapid development. Notable technologies including ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and several variants of Wi-Fi, including the cutting-edge IEEE 802.11ah standards are addressed. The data range and rate, network size, RF channels and bandwidth, power consumption, and other relevant aspects are all studied in depth in the research in order to assess the capabilities and behaviors of these technologies. Evidence suggests that a holistic technology approach is required for the IoT to provide interoperable and secure communications. In this paper, we investigate how recent advances in wireless technology can provide answers to long-standing problems in efficient routing. Several alternative optimization methods, such as PSO and ACO, inspired by natural events have been applied throughout the routing process in previous studies. In this research, we use an MVO to find the optimal connection establishment route for wireless nodes. The simulation results validate that the proposed adjustment improves throughput. In addition, the ratio of packets lost by the conventional method to those lost by the proposed method is larger than that of the latter. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Comparison between Single-Input and Multi-Input Classification with the Application of Canny Feature Extraction and Classification Algorithms on the Toraja Buffalo Dataset 19

Comparison between Single-Input and Multi-Input Classificati...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Manga, Abdul Rachman Nanda, Assyahrin Handayani, Anik Nur Herwanto, Heru Wahyu Asmara, Rosa Andrie Lantara, Dirgahayu Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Information Technology Department State Polytechnic of Malang Malang Indonesia Faculty of Industrial Engineering Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements. © 2025 IEEE.

关键词： Support vector machines

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Confidence-Based Trustscoring System for Identification of Secure Agent Platforms

Confidence-Based Trustscoring System for Identification of S...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Joseph, Adri Jovin John Marikkannu, P. Mariappan, Marikkannan Sri Ramakrishna Institute of Technology Department of Information Technology Coimbatore India Anna University Department of Information Technology Regional Campus Coimbatore Coimbatore India Government College of Engineering Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331509828

Autonomous mobile agent systems can accomplish multiple tasks in an asynchronous and dynamic manner in platforms where there is excessive network load, network interruption and in slow networks. However, the decisions and execution depend on the platforms visited by the agents. Since the platforms may influence the decisions made by the agents, it is crucial that the trustable platforms are selected. Trust score is one parameter that helps mobile agents to select the appropriate platform. This work proposes a minor modification of the existing trust score calculation which has a significant impact on the safety and performance of mobile agent systems when deployed in a conventional distributed environment or in a modern cloud environment for completion of tasks. This work introduces a confidence-based approach for the five parameters namely persistence, competence, reputation, credibility, integrity which determine the trust score. This will provide a clear decision-making environment for the mobile agents to choose an appropriate platform. © 2025 IEEE.

关键词： Cloud platforms

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Fine-Tuned training method for semantic text similarity measurement using SBERT, Bi-LSTM and Attention Network

Fine-Tuned training method for semantic text similarity meas...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Ali, Zafar Aziz, Abdul Ali, Asad Ullah, Amin Aurangzeb, Muhammad Nazir, Muhammad Ahsan Southeast University School of Computer Science and Engineering Nanjing China Southeast University School of Electrical Engineering Nanjing China Southeast University School of Information Science and Engineering Nanjing China

ISBN: (纸本)9798350306545

The Semantic Textual Similarity (STS) problem measures the degree to which two text fragments are similar. In QA systems, it is a crucial tool for extracting useful information from documents. In STS challenges, optimizing lexical gaps between phrases has been a crucial issue. Text similarity may be affected by the distance between major parts of the sentence and the absence of context in the text. Numerous studies have developed answers to this issue. In recent years, however, Sentence-BERT has become widespread in semantic textual similarity tasks. Siamese BERT-Networks generate embeddings that are more context-aware and semantically intact. In addition, BiDirectional LSTM provides a deeper grasp of the input and captures long-range dependencies, whereas the Attention mechanism highlights the most important information aspects from the data. Due to the success of SBERT and Bi-LSTM in exploiting lexical gaps and semantics, we introduce SBERT-Bi-LSTM-Attention (SBiLA), a training model that combines SBERT, Bi-LSTM, and a simple attention strategy. The model improves similarity by addressing the lexical gap between short and long texts and reduces training loss to 0.052, MSE to 0.042, and RMSE to 0.206 on the SQuAD 2.0 and WikiHow datasets. © 2023 IEEE.

关键词： Semantics

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